Biology subjects

Barrera, A.

Publications and source records attributed to Barrera, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Interrogation of noncoding schizophrenia risk variants using CRISPR-based functional genomics

Schizophrenia (SCZ) is a highly heritable complex disorder influenced by coding and noncoding genetic variation. Its genetic causes, particularly those involving noncoding variation, are largely unknown. High-throughput CRISPR screens enable dissection of disease-associated loci and identification of noncoding regulatory elements and variants that modulate gene expression. We screened SCZ GWAS loci linked to genes that are also associated in whole-exome sequencing studies to identify regulatory elements and variants impacting expression of disease-relevant genes. We used CRISPRi paired with HCR-FlowFISH to epigenetically silence 333 putative regulatory elements and measure the downstream effects on gene expression of causal SCZ genes, FAM120A, SV2A, and STAG1, in iPSCs and iPSC-derived neurons (iNeurons). We identified 78 regulatory elements that significantly alter expression of a SCZ gene, including noncoding enhancers/silencers as well as promoters of genes and lncRNAs. Pooled prime editing screens interrogated noncoding variant influence on gene expression for SCZ-associated variants and uncharacterized common variants from diverse population studies. We find that a common variant in the promoter of SV2A, rs112851681:A>G (MAF = 3.56%, 1000 Genomes) enhances transcriptional activity in iPSCs and iNeurons. These findings show distinct noncoding mechanisms that map within GWAS signals, and provide a path forward for interrogating noncoding regulatory elements and variants in disease loci.

genomics↗

bioRxiv2026

Region-Level Design and Analysis of CRISPR Perturbation Screens with FRACTEL

We present FRACTEL, a statistical framework for region-level analysis of CRISPR perturbation screens. FRACTEL aggregates gRNA p-values using a bounded minimum across order statistics, preserving scale and enabling adaptive sensitivity to sparse or diffuse effects. Region-level null distributions are estimated via simulation, ensuring precise type I error control. Simulations and real CRISPRi/a datasets demonstrate improved power and replication rate over gRNA-level analyses. FRACTEL also informs experimental design, revealing trade-offs between gRNA redundancy and efficacy and identifying inherent limits in single-cell repression screens of lowly expressed genes. The method integrates with existing pipelines and supports diverse CRISPR screening applications.

bioinformatics↗

bioRxiv2026

Enhancer hubs govern chromatin topology and Th17 identity

Between one and two million noncoding regulatory elements have been described across mammalian genomes, but determining their functional role remains a challenge. To address this gap, we measure the regulatory potential of open chromatin regions (OCRs) in five closely related mouse CD4+ T cell subsets with ATAC-STARR-seq, then map endogenous enhancer function and three-dimensional contacts in Th17 cells with pooled CRISPR-based noncoding screens and high-resolution Micro-C. Of all CD4+ T cell OCRs, approximately 25% demonstrate largely subset-shared regulatory activity, though we identify subset-restricted active elements distinguishable by their sequence features. In Th17 cells, we reveal a set of core regulatory OCRs essential for subset polarization at the Batf, Rorc(t) and Il17a/f loci, and confirm their requirement for cell identity in vivo. At these three loci, we resolve nested yet selective enhancer-to-enhancer and enhancer-to-promoter physical interactions that converge into multi-enhancer hubs. Importantly, these hubs contain many of the strongest functional elements. Perturbation of a single enhancer within the Batf locus selectively disrupts hub contacts and Batf expression in Th17 cells, recapitulating the genome-wide transcriptional and chromatin accessibility signatures of a germline knockout. Together, this work assigns regulatory activity to accessible chromatin across CD4+ T cell subsets and couples the function of a set of essential elements to selective three-dimensional contacts in Th17 cells.

genomics↗

bioRxiv2026

Modeling gene regulatory perturbations via deep learning from high-throughput reporter assays

Assessing likely variant effects on phenotypes is of critical importance in diagnostic settings, and while much progress has been made in interpreting genic mutations based on our understanding of coding sequence, noncoding variants can be much more challenging to reliably interpret based on DNA sequence alone. High-throughput reporter assays such as STARR-seq and MPRA have shown utility in experimentally measuring regulatory effects of noncoding variants present in samples but provide no readout for variants not present in the assay inputs. However, whole-genome reporter assays provide copious data that can be used to train predictive models for prioritizing variants not directly observed in the experiment. We describe a retrainable predictive modeling framework, BlueSTARR, for this task, and present results of training several models with this framework on whole-genome STARR-seq data from two cell lines and one drug treatment. Using these models, we uncover a global signature across the human genome consistent with purifying selection against both loss-of-function and gain-of-function regulatory variants, with the latter showing a significant bias consistent with selection against gains of cis regulatory function in closed chromatin proximal to genes. By testing the model on synthetic enhancers with binding motifs for transcription factors GR and AP-1, we find that when trained on drug perturbation data, the model is able to learn distance-dependent and treatment-dependent binding patterns and their resulting reporter gene activation. These results demonstrate that lightweight, easily retrainable models such as ours have utility in probing latent signals present in novel experimental data. Finally, we find only modest differences in performance between different deep-learning architectures when trained on this single data modality, and while somewhat greater predictive accuracy can be achieved with much larger models trained at great expense on many terabytes of data, there is still copious room for improvement even for industrial strength, state-of-the-art models.

bioinformatics↗

bioRxiv2026

Structured Pooling Improves Detection of Rare Regulatory Mutations in Population-Scale Reporter Assays

Identifying genetic variants in noncoding DNA that impact gene expression and thereby contribute to disease risk remains a difficult but important challenge in genomic medicine. Modern reporter assays such as STARR-seq and MPRA provide an efficient and effective means of testing, in very high throughput, millions of variants captured directly from patient genomes. While these assays have previously been scaled to whole genomes and, separately, to populations, we report findings from the first whole-genome population-scale STARR-seq experiment performed on 100 individuals. In order to achieve that scale we devised a novel experimental design that partitions samples into pools so as to increase allele frequencies within pools and thereby reduce expected dropout and increase signal-to-noise ratio in experimental readouts. We show that this design produces more accurate estimates of variant effect sizes, and we provide a Bayesian model for robust estimation of those effect sizes that also reports full posterior distributions for assessment of confidence in estimates. Together, these methodological innovations facilitate the detection of functional regulatory variants, particularly rare variants, with much higher accuracy and at greater scale than previously possible. We demonstrate the utility of this approach on the task of functional annotation of quantitative trait loci such as eQTLs and caQTLs, and show concordance with patterns of constraint in transcription factor binding profiles.

bioinformatics↗

bioRxiv2026

Reprogramming of neuronal genome function and phenotype by astrocytes

Heterotypic cell-cell interactions are critical to governing cellular physiology, disease progression, and responses to the environment and pharmacologic interventions. For example, neurons and astrocytes engage in intricate interactions that are essential for brain development and function1-3. However, the transformation of these extracellular signals into epigenomic regulation that governs cell function is poorly understood. Here, we report that weeks of co-culture between human induced pluripotent stem cell (hiPSC)-derived neurons and mouse cortical astrocytes extensively reprograms gene expression and the chromatin accessibility landscape in neurons, affecting thousands of genes and putative gene regulatory elements (REs), including many transcription factors (TFs). These genes are enriched for functions implicated in neuronal differentiation and maturation, and tend to be impacted in schizophrenia, and autosomal dominant Alzheimers disease. Through complementary CRISPR interference and activation screens, we recapitulated hundreds of astrocyte-induced transcriptional and chromatin remodeling events in mono-cultured neurons at both promoters and distal regulatory elements (REs) of TF genes. We discovered functional REs for [~]50 astrocyte-responsive TF genes, providing a map of gene regulatory network control. Astrocyte-responsive TF genes fall into groups that exert independent or counter-balancing transcriptional effects, highlighting the complex coordination of the neuronal response to astrocytes. Functional effects of specific TFs, including POU3F2 and TFAP2E, on neurite morphology and neuronal electrophysiology are consistent with transcriptional effects, demonstrating the capacity of direct epigenetic control to mimic heterotypic cellular signals. This work illuminates the regulation of neurodevelopment-and disease-relevant gene modules by neuron-astrocyte interactions, and provides a blueprint for applying modern functional genomics to uncover the links between cell microenvironment and epigenomic programming. HighlightsO_LINeuronal gene expression and chromatin accessibility landscape are profoundly remodeled by astrocytes over weeks of co-culture C_LIO_LIAstrocyte-responsive neuronal gene modules and neuron-responsive astrocytic gene modules are enriched for genes associated with schizophrenia and familial Alzheimers Disease C_LIO_LISingle-cell CRISPR interference and activation screens of astrocyte-responsive gene regulatory elements identified dozens of functional regulatory elements of TF genes in neurons C_LIO_LISingle-cell CRISPR interference and activation screens of >200 astrocyte-responsive TF genes uncovered discrete functional clusters that promote neuronal maturity or stemness C_LIO_LIAstrocyte-responsive TF genes reprogram neuronal electrophysiology and neurite morphology C_LI

genomics↗

bioRxiv2026

Functional Annotation of the Major Histocompatibility Complex Locus

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis-regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

genomics↗

bioRxiv2025

SMARCA2 is an essential and potent cofactor for a specific subset of the glucocorticoid response in A549 cells

Glucocorticoids are a widely used, potent class of anti-inflammatory drugs that modulate the expression of hundreds of genes across the genome. Although the glucocorticoid response is primarily carried out by the glucocorticoid receptor (NR3C1, a.k.a. GR), there are many glucocorticoid receptor co-factors that are also essential to the downstream effects. To identify novel factors necessary for the glucocorticoid gene expression response, we used a genome-wide CRISPR screen in A549 lung adenocarcinoma cells. In that screen, we knocked out every gene in the human genome, and measured the effect of expression of the glucocorticoid-induced leucine zipper (GILZ), a classic glucocorticoid-response gene. We identified two chromatin remodeling proteins, SMARCA2 and BPTF, that are essential for GILZ expression. We then evaluated the genome-wide effects of SMARCA2 and BPTF on glucocorticoid-mediated gene expression. BPTF had a highly specific role in the glucocorticoid response, affecting the expression of only a handful of genes, and having virtually no effect on dexamethasone-induced changes in chromatin accessibility. However, SMARCA2 was necessary for 27% of dexamethasone-induced transcriptional changes (152 genes), and [~]7% of dexamethasone-induced changes in chromatin accessibility (586 regions of the genome). Genomic regions with SMARCA2-dependent changes in chromatin accessibility were characterized by high dexamethasone-induced regulatory activity in a massively parallel reporter assay, and dexamethasone-induced increases in transcription factor binding and chromatin states. Taken together, these data suggest that SMARCA2 is critical for chromatin remodeling at a specific set of genomic regions with high regulatory activity, which in turn drive changes in expression for many glucocorticoid-responsive genes.

genomics↗

bioRxiv2025

Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow

As researchers and clinicians seek to identify human genomic alterations relevant to traits and disorders, identifying and aggregating evidence providing mechanistic support for associations between alterations and phenotypes remains challenging. In particular, the study of non-coding genomic variation remains a major challenge due to the lack of accurate functional annotation for activity in a given context and across alleles. Experimental evidence is critical for prioritizing and interpreting functional effects of genetic alterations. Massively Parallel Reporter Assays (MPRAs) have emerged as a powerful high-throughput approach, enabling quantification of regulatory element activity and allelic effects, and systematic dissection of gene regulatory logic and variant effects across different contexts. However, the diversity of MPRA designs, lack of standardized formats, and many potential processing parameters hamper data integration, reproducibility, and meta-analyses across studies. To address these challenges, the Impact of Genomic Variation on Function (IGVF) Consortium established an MPRA focus group to develop community standards, including harmonized file formats, and robust analysis pipelines for a wide range of library types and experimental designs. Here, we present these formats and comprehensive computational tools, MPRAlib and MPRAsnakeflow, for uniform processing from raw sequencing reads to counts, processing and visualization. Using diverse MPRA datasets, we characterize technical variability sources including barcode sequence bias, outlier barcodes, and delivery method (episomal vs. lentiviral). Our results establish best practices for MPRA data generation and analysis, facilitating robust, reproducible research and large-scale integration. The presented tools and standards are publicly available, providing a foundation for future collaborative efforts in regulatory genomics.

genomics↗

bioRxiv2024

DISTRIBUTION OF LIVING (ROSE BENGAL STAINED) BENTHIC FORAMINIFERA IN COPANO, ARANSAS, AND REDFISH BAYS, CENTRAL TEXAS COAST

A combination of human development and climate change have the potential to impact ecosystems in the ecologically, economically, and culturally important bays of the Texas Coastal Bend in the northwestern Gulf of Mexico. To determine the extent to which potential impacts are becoming actual impacts, population studies are necessary. Here, we report populations for living benthic foraminifera from three bays collected in May 2023 and 2024. We find the bays largely dominated by Ammonia, a shift from the last complete census carried out by Frances Parker and colleagues in 1951 during a multiyear drought. We see no correlation between predominance facies and salinity within our samples, although we only record a salinity range of 25-30 PSU, a small fraction of the possible range of [~]10-40 PSU in these bays. We do observe a relationship between sediment grain size and predominance facies, with Ammonia more common in finer grained environments.

ecology↗

bioRxiv2024

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/611293v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@165c63dorg.highwire.dtl.DTLVardef@ba0e15org.highwire.dtl.DTLVardef@f2b12eorg.highwire.dtl.DTLVardef@14e6c86_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Gene regulatory activity associated with PCOS revealed DENND1A-dependent testosterone production

Polycystic ovary syndrome (PCOS) is among the most common disorders affecting up to 15% of the menstruating population globally. It is the leading cause of anovulatory infertility and a major risk factor for type 2 diabetes. Elevated testosterone levels are a core endophenotype. Despite that prevalence, the underlying causes remain unknown. PCOS genome-wide association studies (GWAS) have reproducibly mapped a number of susceptibility loci, including one encompassing a gene regulating androgen biosynthesis, DENND1A. Identifying the causal variants within these loci will provide fundamental insight into the precise biological pathways that are disrupted in PCOS. We report the discovery of gene regulatory mechanisms that help explain genetic association with PCOS in the GATA4, FSHB and DENND1A loci using a combination of high throughput reporter assays, CRISPR-based epigenome editing, and genetic association analysis from PCOS case and control populations. In addition, we found that increased endogenous DENND1A expression causes elevated testosterone levels in an adrenal cell model, specifically by perturbing candidate regulatory elements. These results further highlight the potential for combining genetic variant analyses with experimental approaches to fine map genetic associations with disease risk.

genomics↗

bioRxiv2024

Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments

CRISPR epigenomic editing technologies enable functional interrogation of non-coding elements. However, current computational methods for guide RNA (gRNA) design do not effectively predict the power potential, molecular and cellular impact to optimize for efficient gRNAs, which are crucial for successful applications of these technologies. We present "launch-dCas9" (machine LeArning based UNified CompreHensive framework for CRISPR-dCas9) to predict gRNA impact from multiple perspectives, including cell fitness, wild-type abundance (gauging power potential), and gene expression in single cells. Our launch-dCas9, built and evaluated using experiments involving >1 million gRNAs targeted across the human genome, demonstrates relatively high prediction accuracy (AUC up to 0.81) and generalizes across cell lines. Method-prioritized top gRNA(s) are 4.6-fold more likely to exert effects, compared to other gRNAs in the same cis-regulatory region. Furthermore, launch-dCas9 identifies the most critical sequence-related features and functional annotations from >40 features considered. Our results establish launch-dCas9 as a promising approach to design gRNAs for CRISPR epigenomic experiments.

bioinformatics↗

bioRxiv2024

Activation of the imprinted Prader-Willi Syndrome locus by CRISPR-based epigenome editing

SummaryEpigenome editing with DNA-targeting technologies such as CRISPR-dCas9 can be used to dissect gene regulatory mechanisms and potentially treat associated disorders. For example, Prader-Willi Syndrome (PWS) is caused by loss of paternally expressed imprinted genes on chromosome 15q11.2-q13.3, although the maternal allele is intact but epigenetically silenced. Using CRISPR repression and activation screens in human induced pluripotent stem cells (iPSCs), we identified genomic elements that control expression of the PWS gene SNRPN from the paternal and maternal chromosomes. We showed that either targeted transcriptional activation or DNA demethylation can activate the silenced maternal SNRPN and downstream PWS transcripts. However, these two approaches function at unique regions, preferentially activating different transcript variants and involving distinct epigenetic reprogramming mechanisms. Remarkably, transient expression of the targeted demethylase leads to stable, long-term maternal SNRPN expression in PWS iPSCs. This work uncovers targeted epigenetic manipulations to reprogram a disease-associated imprinted locus and suggests possible therapeutic interventions.

genomics↗

bioRxiv2024

Mechanosensitive genomic enhancers potentiate the cellular response to matrix stiffness

Epigenetic control of cellular transcription and phenotype is influenced by changes in the cellular microenvironment, yet how mechanical cues from these microenvironments precisely influence epigenetic state to regulate transcription remains largely unmapped. Here, we combine genome-wide epigenome profiling, epigenome editing, and phenotypic and single-cell RNA-seq CRISPR screening to identify a new class of genomic enhancers that responds to the mechanical microenvironment. These mechanoenhancers could be active on either soft or stiff extracellular matrix contexts, and regulated transcription to influence critical cell functions including apoptosis, mechanotransduction, proliferation, and migration. Epigenetic editing of mechanoenhancers on rigid materials tuned gene expression to levels observed on softer materials, thereby reprogramming the cellular response to the mechanical microenvironment. These editing approaches may enable the precise alteration of mechanically-driven disease states.

genomics↗

bioRxiv2023

Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function

The clinical response to adoptive T cell therapies is strongly associated with transcriptional and epigenetic state. Thus, technologies to discover regulators of T cell gene networks and their corresponding phenotypes have great potential to improve the efficacy of T cell therapies. We developed pooled CRISPR screening approaches with compact epigenome editors to systematically profile the effects of activation and repression of 120 transcription factors and epigenetic modifiers on human CD8+ T cell state. These screens nominated known and novel regulators of T cell phenotypes with BATF3 emerging as a high confidence gene in both screens. We found that BATF3 overexpression promoted specific features of memory T cells such as increased IL7R expression and glycolytic capacity, while attenuating gene programs associated with cytotoxicity, regulatory T cell function, and T cell exhaustion. In the context of chronic antigen stimulation, BATF3 overexpression countered phenotypic and epigenetic signatures of T cell exhaustion. CAR T cells overexpressing BATF3 significantly outperformed control CAR T cells in both in vitro and in vivo tumor models. Moreover, we found that BATF3 programmed a transcriptional profile that correlated with positive clinical response to adoptive T cell therapy. Finally, we performed CRISPR knockout screens with and without BATF3 overexpression to define co-factors and downstream factors of BATF3, as well as other therapeutic targets. These screens pointed to a model where BATF3 interacts with JUNB and IRF4 to regulate gene expression and illuminated several other novel targets for further investigation.

genomics↗

bioRxiv2022

Multi-center integrated analysis of non-coding CRISPR screens

The ENCODE Consortiums efforts to annotate non-coding, cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes which play a major role in health and disease. Pooled, non-coding CRISPR screens are a promising approach for systematically investigating gene regulatory mechanisms. Here, the ENCODE Functional Characterization Centers report 109 screens comprising 346,970 individual perturbations across 13.3Mb of the genome, using a variety of methods, readouts, and statistical analyses. Across 332 functionally confirmed CRE-gene links, we identify principles for screening endogenous, non-coding elements for causal regulatory mechanisms. Nearly all CREs show strong evidence of open chromatin, and targeting accessibility peak summits is a critical component of our proposed sgRNA design rules. We provide experimental guidelines to accurately detect CREs with variable, often low, transcriptional effects. We discover a previously undescribed DNA strand-bias for CRISPRi in transcribed regions with implications for screen design and analysis. Benchmarking five screen analysis tools, we find CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity sgRNAs. Together, we provide an accessible data resource, predesigned sgRNAs targeting 3,275,697 ENCODE SCREEN candidate CREs, and screening guidelines to accelerate functional characterization of the non-coding genome.

genomics↗

bioRxiv2022

Mediator Kinase Inhibition Impedes Transcriptional Plasticity and Prevents Resistance to ERK/MAPK-Targeted Therapy in KRAS-Mutant Cancers

Acquired resistance remains a major challenge for therapies targeting oncogene activated pathways. KRAS is the most frequently mutated oncogene in human cancers, yet strategies targeting its downstream signaling kinases have failed to produce durable treatment responses. Here, we developed multiple models of acquired resistance to dual-mechanism ERK/MAPK inhibitors across KRAS-mutant pancreatic, colorectal, and lung cancers, and then probed the long-term events enabling survival against this novel class of drugs. These studies revealed that resistance emerges secondary to large-scale transcriptional adaptations that are diverse and tumor-specific. Transcriptional reprogramming extends beyond the well-established early response, and instead represents a dynamic, evolved population-level process that is refined to attain a stably resistant phenotype. Mechanistic and translational studies reveal that resistance to dual-mechanism ERK/MAPK inhibition is broadly susceptible to manipulation of the epigenetic machinery, and that Mediator kinase, in particular, can be co-targeted at a bottleneck point to prevent diverse, tumor-specific resistance programs.

cancer biology↗